Biblio

We have several issues in most current supply chain management systems. Consumers want to spend money on environmentally friendly products, but they are seldomly informed of the environmental contributions of the suppliers. Meanwhile, each supplier seeks to recover the costs for the environmental contributions to re-invest them into further contributions. Instead, in most current supply chains, the reward for each supplier is not clearly defined and fairly distributed. To address these issues, we propose a supply-chain contribution management platform for fair reward distribution called ‘Advanced Ledger.’ This platform records suppliers' environ-mental contribution trails, receives rewards from consumers in exchange for trail-backed fungible tokens, and fairly distributes the rewards to each supplier based on the contribution trails. In this paper, we overview the architecture of Advanced Ledger and 11 technical features, including decentralized autonomous organization (DAO) based contribution verification, contribution concealment, negative-valued tokens, fair reward distribution, atomic rewarding, and layer-2 rewarding. We then study the requirements and candidates of the smart contract platforms for implementing Advanced Ledger. Finally, we introduce a use case called ‘ESG token’ built on the Advanced Ledger architecture.

The study exercises computational linguistics, specifically chemical linguistics methods for profiling an author. We analyze the vocabulary and the style of the titles of the most visible works of Cristofor I. Simionescu, an internationally well-known chemist, for detecting specific patterns of his research interests and methods. Somewhat surprisingly, while the tools used are elementary and there is only a small number of words in the analysis, some interesting details emerged about the work of the analyzed personality. Some of these aspects were confirmed by experts in the field. We believe this is the first study aiming to author profiling in chemical linguistics, moreover the first to question the usefulness of Google Scholar for author profiling.

In the digital payment system, the transactions and their data about clients are very sensitive, so the security and privacy of personal information of the client is a big concern. The confirmation towards security necessities prevents the data from a stolen and unauthorized person over the digital transactions, So the stronger authentication methods required, which must be based on cryptography. Initially, in the payment ecosystem, they were using the Kerberos protocol, but now different approaches such as Challenge-Handshake Authentication Protocol (CHAP), Tokenization, Two-Factor Authentication(PIN, MPIN, OTP), etc. such protocols are being used in the payment system. This paper presents the use cases of different authentication protocols. Further, the use of these protocols in online payment systems to verify each individual are explained.

The exponential growth of the Internet and its users and the emergence of Web 2.0 have caused a large volume of textual data to be created. Automatic analysis of such data can be used in making decisions. As online text is created by different producers with different styles of writing, pre-processing is a necessity prior to any processes related to natural language tasks. An essential part of textual preprocessing prior to the recognition of the word vocabulary is normalization, which includes the correction of spaces that particularly in the Persian language this includes both full-spaces between words and half-spaces. Through the review of user comments within social media services, it can be seen that in many cases users do not adhere to grammatical rules of inserting both forms of spaces, which increases the complexity of the identification of words and henceforth, reducing the accuracy of further processing on the text. In this study, current issues in the normalization and tokenization of preprocessing tools within the Persian language and essentially identifying and correcting the separation of words are and the correction of spaces are proposed. The results obtained and compared to leading preprocessing tools highlight the significance of the proposed methodology.

Thanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.